Efficient Function Queryable and Privacy Preserving Data Aggregation Scheme in Smart Grid

被引:24
作者
Zhan, Yu [1 ,2 ]
Zhou, Liguo [1 ,2 ]
Wang, Baocang [1 ,2 ]
Duan, Pu [3 ]
Zhang, Benyu [3 ]
机构
[1] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China
[2] Xidian Univ, Cryptog Res Ctr, Xian 710071, Peoples R China
[3] Ant Grp, Secure Collaborat Intelligence Lab, Hangzhou 310007, Peoples R China
关键词
Data aggregation; Smart grids; Smart meters; Data privacy; Costs; Real-time systems; Privacy; Fog computing; function queryable; homomorphic encryption; privacy-preserving; PUBLIC-KEY CRYPTOSYSTEM; DEMAND RESPONSE; SECURE; COMMUNICATION; FRAMEWORK;
D O I
10.1109/TPDS.2022.3153930
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The collection of users' near-real-time electricity consumption data brings advantages to the operation of smart grids, while raising some security and privacy issues. Multiple privacy preserving data aggregation schemes have been proposed to address these problems. However, most schemes only focus on the aggregation of electricity consumption data without considering the data availability. In addition, although a data aggregation scheme that supports function queries on encrypted data has also been developed, its efficiency is insufficient. In this paper, we first propose an EC-ElGamal encryption algorithm with a double trapdoor decryption mechanism. Through employing the proposed algorithm and the elliptic curve Schnorr signature scheme, an efficient data aggregation scheme supporting privacy protection and function query is proposed for smart grids. This solution allows the control center and users to initiate various function queries on encrypted data. In order to lighten the calculation burden of the control center, we propose another cryptosystem named ElGamal-OU to improve the decryption efficiency, which also supports two independent decryption methods. Finally, the security analysis and performance comparison with related work show that our schemes have advantages in terms of computational, communication and storage overhead.
引用
收藏
页码:3430 / 3441
页数:12
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